Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

In the problem of unsupervised domain adaption Extreme learning machine (ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive ability. Experiments on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.     

Regularized manifold information extreme learning machine

By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold information of data to perform network of ELM. The proposed algorithm could overcome the problem of the overlap of information. Singular problems of inter-class and within-class were solved effectively by using maximum margin criterion. The problem of inadequate learning with limited samples was solved. In order to demonstrate the effectiveness, comparative experiments with ELM and the related update algorithms RAFELM, GELM were conducted using the commonly used image data. Experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM and outperforms the related update algorithms.     

Robotic grasping recognition using multi-modal deep extreme learning machine

Recognizing which part of an object is graspable or not is important for intelligent robot to perform some complicated tasks. In order to obtain good grasping performance, learning rich representations efficiently from multi-modal RGB-D images is crucial. To address this problem, in this paper, we propose an effective multi-modal deep extreme learning machine structure. In this structure, unsupervised hierarchical extreme learning machine (ELM) is conducted for feature extraction for RGB and depth modalities separately. Then, the shared layer is developed by combining both RGB and depth features. Finally, the ELM is used as supervised feature classifier for final decision. Experimental validation on Cornell grasping dataset illustrates that the proposed multiple modality fusion method achieves better grasp recognition performance.     

A pruning ensemble model of extreme learning machine with $$L_{1/2}$$ regularizer

Extreme learning machine (ELM) as an emerging branch of machine learning has shown its good generalization performance at a very fast learning speed. Nevertheless, the preliminary ELM and other evolutional versions based on ELM cannot provide the optimal solution of parameters between the hidden and output layer and cannot determine the suitable number of hidden nodes automatically. In this paper, a pruning ensemble model of ELM with \(L_{1/2} \) regularizer (PE-ELMR) is proposed to solve above problems. It involves two stages. First, we replace the original solving method of the output parameter in ELM to a minimum squared-error problem with sparse solution by combining ELM with \(L_{1/2}\) regularizer. Second, in order to get the required minimum number for good performance, we prune the nodes in hidden layer with the ensemble model, which reflects the superiority in searching the reasonable hidden nodes. Experimental results present the good performance of our method PE-ELMR, compared with ELM, OP-ELM and PE-ELMR (L1), for regression and classification problems under a variety of benchmark datasets.     

基于极限学习机的目标智能威胁感知技术

空中目标威胁感知是智能空战的关键技术之一。针对空中目标威胁评估问题,提出一种基于极限学习机的目标威胁智能感知策略。该方法首先提取目标威胁评估的高权重态势因子,并采用隶属度函数进行归一化数值解译;然后,借助专家知识采用极限学习机理论对威胁感知的输入输出数据进行建模,构建智能感知推理模型;最后,建立基于极限学习机的目标威胁智能感知流程。仿真结果表明,该算法具有较高的威胁感知精度以及较好的算法实时性。     

基于双错测度的极限学习机选择性集成方法

极限学习机(ELM)具有学习速度快、易实现和泛化能力强等优点,但单个ELM的分类性能不稳定。集成学习可以有效地提高单个ELM的分类性能,但随着数据规模和基ELM数目的增加,计算复杂度会大幅度增加,消耗大量的计算资源。针对上述问题,该文提出一种基于双错测度的极限学习机选择性集成方法(DFSEE),同时从理论和实验的角度进行了详细分析。首先,运用bootstrap 方法重复抽取训练集,获得多个训练子集,在ELM上进行独立训练,得到多个具有较大差异性的基ELM,构成基ELM池;其次,计算出每个基ELM的双错测度,将基ELM按照双错测度的大小进行升序排序;最后,采用多数投票算法,根据顺序将基ELM逐个累加集成,直至集成精度最优,即获得基ELM最优子集成,并分析了其理论基础。在10个UCI数据集上的实验结果表明,较其他方法使用了更小规模的基ELM,获得了更高的集成精度,同时表明了其有效性和显著性。     

一种稳定的基于极端学习机的纹理分类方法

针对传统基于极端学习机(ELM)的纹理分类方 法容易出现输出不稳定的缺点,本文将线性和非线 性模型进行融合,改进了传统动力学模型。利用ELM能够快速学习的特点,将其作为基分类 器,同时利用 线性吸引子和局部吸引子的迭代,实现多个ELM分类器的最佳融合,以提升学习模型的泛 化能力和稳定 性。利用动力模型实现多分类器的融合有助于寻求多个基分类器之间的一致性,摒弃了基分 类器中判别错 误的样本输出。通过对CUReT纹理数据库的分类结果,与传统纹理分类方法相 比,本文算法的稳定性 和分类准确率都得到了一定程度的提升,取得了理想的分类效果。     

Extreme learning machine approach for sensorless wind speed estimation

Precise predictions of wind speed play important role in determining the feasibility of harnessing wind energy. In fact, reliable wind predictions offer secure and minimal economic risk situation to operators and investors. This paper presents a new model based upon extreme learning machine (ELM) for sensor-less estimation of wind speed based on wind turbine parameters. The inputs for estimating the wind speed are wind turbine power coefficient, blade pitch angle, and rotational speed. In order to validate authors compared prediction of ELM model with the predictions with genetic programming (GP), artificial neural network (ANN) and support vector machine with radial basis kernel function (SVM-RBF). This investigation analyzed the reliability of these computational models using the simulation results and three statistical tests. The three statistical tests includes the Pearson correlation coefficient, coefficient of determination and root-mean-square error. Finally, this study compared predicted wind speeds from each method against actual measurement data. Simulation results, clearly demonstrate that ELM can be utilized effectively in applications of sensor-less wind speed predictions. Concisely, the survey results show that the proposed ELM model is suitable and precise for sensor-less wind speed predictions and has much higher performance than the other approaches examined in this study.     

Transfer Learning of BCI Using CUR Algorithm

The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject’s EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression.

     

K插值单纯形法核极限学习机的研究

针对核极限学习机高斯核函数参数选优难,影响学习机训练收敛速度和分类精度的问题,该文提出一种K插值单纯形法的核极限学习机算法。把核极限学习机的训练看作一个无约束优化问题,在训练迭代过程中,用Nelder-Mead单纯形法搜索高斯核函数的最优核参数,提高所提算法的分类精度。引入K插值为Nelder-Mead单纯形法提供合适的初值,减少单纯形法的迭代次数,提高了新算法的训练收敛效率。通过在UCI数据集上的仿真实验并与其它算法比较,新算法具有更快的收敛速度和更高的分类精度。     

极限学习机方法在经纬仪空间配准中的应用

针对光电经纬仪测量中多传感器的空间配准问题,提出了一种基于极限学习机（ELM）的空间配准建模方法。首先介绍了ELM算法和ELM空间配准模型的建立步骤,然后使用星体测量数据建立ELM空间配准模型,最后将该模型与单项差修正模型、球谐函数修正模型进行了对比验证。实验结果表明:ELM空间配准模型可以使光电经纬仪的测量精度从17左右提高到1以内,与单项差修正模型、球谐函数修正模型相比精度提高35%以上。由此可见,与单项差修正模型和球谐函数修正模型相比,采用ELM算法所建立的光电经纬仪空间配准模型具有更高的精度和更强的泛化能力。     

基于分块CNN的多尺度SAR图像目标分类算法

针对合成孔径雷达（Synthetic Aperture Radar, SAR）图像目标分辨率差异大,多尺度SAR图像目标分类准确率不高的问题,提出了一种基于迁移学习和分块卷积神经网络（Convolutional Neural Network, CNN）的SAR图像目标分类算法。首先通过大量与目标域相近的源域数据对分块CNN的参数进行训练,得到不同尺度下的CNN特征提取网络;其次将CNN的卷积和池化层迁移到新的网络结构中,实现目标特征的提取;最后用超限学习机（Extreme Learning Machine, ELM）网络对提取的特征进行分类。实验数据采用美国MSTAR数据库以及多尺度SAR图像舰船目标数据集,实验结果表明,该方法对多尺度SAR图像的分类效果优于传统CNN。     

基于特征提取相似日的ELM短期负荷预测研究

为解决短期电力负荷预测中预测精度差、计算时间长等问题,提出一种基于自组织特征映射网络进行特征提取相似日的极限学习机短期电力负荷预测方法。通过自组织特征映射网络找出与预测日同类型的历史数据作为训练样本;并采用预测能力强、计算时间短的ELM网络进行预测。以某市电力负荷数据进行仿真,并将上述方法与传统神经网络进行对比。仿真算例表明,基于特征提取相似日的ELM方法具有较高的预测精度,泛化性能好,且运算时间短。     

无人机辅助的基于前馈神经网络的节点定位算法

针对无线传感网络(WSNs)的节点定位问题,提出无人机辅助的基于前馈神经网络的节点定位(UAV-NN)算法。UAV-NN算法利用无人机(UAV)作为锚节点,并由UAV周期地发射beacon信号,利用极端学习机(LEM)训练单隐藏前向反馈的神经网络(SLFN),未知节点接收来自UAV发射的beacon信号,并记录其接收信号强度指示(RSSI),已训练的SLFN再依据RSSI值估计节点位置。仿真结果表明,相比于传统的基于RSSI定位算法,提出的UAV-NN算法无需部署地面锚节点;相比其他传统的机器学习算法,UAV-NN算法通过引用ELM,减少了定位误差。     

基于HOGv-CLBP特征融合和ELM的交通标志识别

针对交通标志识别识别率低和时间复杂度大的问题 ,本文提出一种HOGv-CLBP特征融合和极限学习机的交通标志识别算法。首先通过描述交通 标志图像边缘信息的方向梯度直方图(HOG)特征,与能够 表示标志图像内部纹理信息的局部二值模式(LBP)特征融合得到降维后形成一种HOGv-CLB P有效特征, 然后利用ELM进行交通标志训练和分类。实验结果表明,该算法不仅提高了交通标志的识别 率,而且降低了时间复杂度,增强了系统鲁棒性。     

An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature

Underground pipeline network surveillance system attracts increasingly attentions recently due to severe breakages caused by external excavation equipments in the mainland of China. In this paper, we study excavation equipments classification algorithm based on acoustic signal processing and machine learning algorithms. A cross-layer microphone array with four elements is designed to collect the acoustic database of representative excavation equipments on real construction sites. The generalized sidelobe canceller algorithm is employed for background noise reduction. The improved spectrum dynamic feature extraction algorithm is then implemented for the benchmark acoustic feature database construction of excavation equipments. To perform classification and background noise identification, the single hidden layer feedforward neural network is employed as the classifier. An improved algorithm based on the popular extreme learning machine (ELM) is proposed for classifier learning. The leave-one-out cross validation strategy is adopted for the regularization parameter optimization in ELM. Comprehensive experiments are conducted to test the effectiveness of the proposed algorithm. Comparisons with state-of-art classifiers and the Mel-frequency cepstrual coefficients acoustic features are also provided to demonstrate the superiority of our approach.     

基于B2DPCA和极端学习机的人脸识别

介绍一种新的基于双向二维主成分分析(B2DPCA)和极端学习机(ELM)的人脸识别方法,该方法是根据人脸曲波图像分解和一种改进的降维技术,通过B2DPCA生成识别特征集来提高分类精度.该方法还能够有效地提高分类正确率和降低对原型数量的依赖.通过做大量的实验,把结果和现存技术相比较.     

结合多特征融合和极限学习机的红外图像目标分类方法

针对红外图像目标分类问题,提出了结合多特征融合和极限学习机（extreme learning machine,ELM）的方法。采用主成分分析（principal component analysis,PCA）、局部二值模式（local binary pattern,LBP）以及尺度不变特征变换（scale-invariant feature transform,SIFT）三类特征分别描述红外图像中目标的像素分布、局部纹理以及特征点信息。三类特征从不同侧面反映红外图像目标特性,因此具有互为补充的优势。在此基础上,基于多重集典型相关分析（multiset canonical correlations analysis,MCCA）对三类特征进行融合处理,获得统一的特征矢量。融合后的特征不仅继承了原始三类特征的鉴别特性,还有效去除了冗余信息。分类过程中,采用极限学习机作为基础分类器对融合特征矢量进行分类。极限学习机具有参数少、效率高、精度高和稳健性强等显著特点,有利于提高红外目标分类的整体性能。因此,所提出的方法通过结合多特征和极限学习机的优势综合提升了目标识别性能。在实验过程中,采用四类飞机目标的红外图像对所提出方法进行了性能测试。根据与现有几类方法的对比,实验结果证明了提出方法的性能优势。